Development of a fatty acid metabolism-related genes predictive model in colon cancer patients

doi:10.21203/rs.3.rs-3085478/v1

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Development of a fatty acid metabolism-related genes predictive model in colon cancer patients

https://doi.org/10.21203/rs.3.rs-3085478/v1

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Purpose

Colon cancer (CC) is a malignant tumor with high morbidity and mortality. Fatty acid metabolism, has attracted more attention as an essential part of tumor metabolic reprogramming. This study aimed to investigate the relationship between fatty acid metabolism-related genes and clinical survival outcomes in CC.

Method

We downloaded the mRNA expression profiles and clinical information of CC from the TCGA data portal. Expression of fatty acid metabolism-related genes and survival data of CC samples were extracted. Univariate Cox analysis and LASSO regression analysis were used to identify the fatty acid metabolism-related genes correlated with the prognosis of CC patients. Then, those six prognostic fatty acid metabolism-related genes were used to construct a prognostic model to predict the survival probability of CC patients. Patients were divided into two groups at high and low risk, and the differences in GSEA enrichment, drug sensitivity, immune cell infiltration, the efficacy of immunotherapy, and immune checkpoint expression level between the two groups were discussed. Finally, a novel nomogram integrating the risk score, age, gender, and clinical stages was established to predict the prognosis of CC patients. The Nomogram prediction model's accuracy was evaluated by using calibration plots, ROC curve, and DCA.

Result

449 CC and 41 normal samples were included in this study. A prognostic model based on six fatty acid metabolism-related genes was built to evaluate the prognosis of CC patients. Patients in the high-risk group had poorer overall survival than those in the low-risk group (P < 0.001). The expression level of macrophages and T helper cells were higher, and the expression level of Tregs was lower in the high-risk group. The expression levels of PD-1, LAG3, and CTLA4 were higher in high-risk patients, and the high-risk group had a higher TIDE score, indicating a worse response to immunotherapy. The Calibration plots, ROC curve, and DCA have all proved that the Nomogram system can accurately predict the survival rate of CC patients.

Conclusion

Fatty acid metabolism-related genes can be used as a new therapeutic target for CC and further improve the survival rate of CC patients through individualized therapy.

Fatty acid metabolism

Predictive model

Colon cancer

Immune

Colon cancer (CC) is one of the significant gastrointestinal malignancies that threaten human life and health. It ranks third in the incidence of malignant tumors in the world, after lung cancer and breast cancer, and second in the death rate of malignant tumors, after lung cancer[1]. To improve the long-term survival rate and the life quality of CC patients, comprehensive treatments such as chemotherapy, radiotherapy, and surgical treatment are increasingly applied. However, although treatment strategies have developed rapidly in the past few years, the five-year survival rate of CC patients is not more than 65%, and the prognosis of CC is different in various stages [2, 3]. Therefore, it is urgent to explore novel predictive models to predict the prognosis, thus individualizing the treatment and management of CC patients.

Metabolic reprogramming is one of the characteristics of tumor cells. Due to the rapid growth, the demand of tumor cells for nutrients increases, and thus tumor cells undergo metabolic reprogramming to meet their energy needs[4]. Fatty acid metabolism, as one of the important components of tumor metabolic reprogramming, has received significant attention[5]. Fatty acid metabolism includes fatty acid biosynthesis and oxidation, both of which play an indispensable role in tumor cell proliferation and migration, tumor angiogenesis, and intracellular signaling transduction[6, 7]. In recent years, targeted fatty acid synthesis can be used as a new approach to tumor therapy[8, 9]. Many fatty acid synthesis inhibitors are in the pre-clinical and clinical development stages, including Cerulenin (FASN inhibitor), TOFA, and Soraphen A (ACC inhibitors) [10]. Furthermore, the exploration of CPT1, a key enzyme targeting fatty acid oxidation, is also increasing yearly[11–13]. As a CPT1 inhibitor, Etomoxir, has long been used in treating type 2 diabetes and chronic heart failure[14].

In recent years, it has been found that fatty acid infiltration in the tumor microenvironment can affect the phenotype and function of immune cells. For example, fatty acid can induce M2 polarization of macrophages, presenting an immunosuppressive phenotype, and blocking fatty acid oxidation can reverse the immunosuppressive phenotype of macrophages and inhibit the growth of hepatoma cells[15]. Moreover, fatty acid oxidation-related enzymes and immune checkpoints play a synergistically role in tumor cell growth. Enhanced fatty acid metabolism promote cd47-mediated immune escape GBM aggressive growth. The combined use of fatty acid oxidation inhibitors and immune checkpoint inhibitors can control tumor growth to a large extent[16]. However, the prognostic value of fatty acid metabolism-related genes and their relationship with immunotherapy is poorly understood.

In our study, we found that six fatty acid metabolism-related genes (ENO3, ELOVL3, ACOT11, ALAD, ELOVL6, ACADL) were related to the survival outcomes of CC patients. Based on these six genes, a prognostic model was built to evaluate the prognosis of CC patients. Patients were divided into two groups at high and low risk, and the differences in GSEA enrichment, drug sensitivity, immune cell infiltration, the efficacy of immunotherapy, and immune checkpoint expression level between the two groups were discussed. Finally, a novel nomogram model incorporated with fatty acid metabolism-related genes and clinical factors was established to explore the prognosis of CC patients further. In conclusion, fatty acid metabolism-related genes can be used as a new therapeutic target for CC and further improve the survival rate of CC patients through individualized treatment.

Data Processing

The mRNA expression profile and corresponding clinical information of CC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/). Finally, 41 normal colon samples and 449 CC cases were included in this study. Additionally, the following clinical information was extracted for further analyses: gender, age, stage, T, N, M classification, survival status, and follow-up time. Data were extracted and arranged by R and Perl programming languages.

Obtain fatty acid metabolism-related genes

Three gene sets related to fatty acid metabolism were obtained from Hallmark fatty acid metabolism gene, KEGG fatty acid metabolism pathway, and Reactome fatty acid metabolism gene, and the fatty acid metabolism gene set was obtained by removing overlapping genes.

Fatty acid metabolism-related prognostic model in CC patients

In order to identify fatty acid metabolism-related genes associated with survival of the CC patients, we further performed univariate and multivariate Cox regression analyses based on corresponding expression and survival data. Those genes associated with prognosis in the multivariate Cox regression analysis have proceeded in the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The independent prognostic fatty acid metabolism-related genes were selected to construct the prognostic model. The coefficients of these regulators were selected according to the optimal penalty parameter λ in the cross-validation method. The following formula was used to estimate the risk score of each CC patient: risk score = (coef1 × expression of gene 1) + (coef2 × expression of gene 2) + (coef3 × expression of gene 3) +…+ (coefn × expression of gene n), where “coef” refers to the coefficient, and expression of genes was the relative value of selected genes. The mean risk score was used as the cutoff value to separate patients into low- and high-risk groups.

Drug sensitivity analysis

ssGSEA and TIDE

ssGSEA is implemented through the GSVA R package, quantitative immune cell infiltration degree of each sample, using the TIDE (http://tide.dfci.harvard.edu/) algorithm to calculate the corresponding TIDE scores to predict the effectiveness of the patients with immunotherapy.

Establishment of nomogram system

Nomogram is a graph using parallel scales for different variables so that a total point of all variables can be calculated directly, and the risk of event occurrence can be read based on the total point. The nomogram model was built to integrate the risk score, age, gender, and clinical stages. Firstly, univariate and multivariate Cox regression analyses were used to identify factors associated with the prognosis of CC. Secondly, this this nomogram model enrolled factors including age, gender, risk score, T status, N status, M status, and AJCC stage. The calibration plots of the nomogram system were used to evaluate whether the predicted survival probability is consistent with the actual probability, which showed the accuracy of the prediction. The area under the ROC curves was also calculated to assess the efficiency of the nomogram prediction system.

Statistical analysis

Statistical analyses were conducted using R software (version 4.0.3). Cox regression analysis was performed to identify the prognostic fatty acid metabolism-related genes. The survival analysis was applied to explore the prognostic difference between groups. R software was used to draw the heatmaps, forest, and calibration plots. P-value < 0.05 was considered statistically significant.

Characteristics of CC patients from the TCGA database

In order to analyze the relationships between the expression of fatty acid metabolism-related genes and the survival of CC patients, thus underlining the prognostic value of these fatty acid metabolism-related genes, we obtained the transcriptome and clinical profiles of CC patients from the TCGA database in this study. The following variables were extracted for further analysis: gender, age, grade, stage, T, N, and M classifications, survival status, and time. In total, 449 CC and 41 normal samples were included. More details are summarized in Table 1.

Table 1

Clinicopathological characteristics of CC patients from TCGA database.
Characteristics	Number (%)
Age (years)
≤ 65	190 (41.39)
> 65	269 (58.61)
Gender
Male	243 (52.94)
Female	216 (47.06)
T stage
T1-2	89 (19.39)
T3-4	369 (80.39)
Unknow	1 (0.22)
N stage
N0	270 (58.82)
N1-3	189 (41.18)
M stage
M0	337 (73.42)
M1	65 (14.16)
unknow	57 (12.42)
UICC stage
Stage I-II	254 (55.34)
Stage III-IV	194 (42.26)
Unknow	11 (2.40)
Survival status
Alive	357 (77.78)
Dead	102 (22.22)

Identification of fatty acid metabolism-related genes in patients with CC

This study identified fatty acid metabolism-related genes for CC patients according to previous studies[17]. The intersections of three groups of genes related to fatty acid metabolism were filtered, and 309 genes were extracted after removing overlapping genes (Fig. 1A). Furthermore, 158 differentially expressed genes were subsequently identified between the two groups.

Univariate and multivariate Cox proportional risk regression analyses were used to evaluate the relationship between fatty acid metabolism gene expression level and prognosis of CC patients in the TCGA (Fig. 1B and C). A total of 6 genes associated with prognosis were identified (P༜0.05), and the prognostic information of these six (6) genes is shown in Fig. 1C.

A prognostic model established with fatty acid metabolism-related genes in CC

These 6 genes were further evaluated by LASSO analysis to decide the number of variables in the prognostic model. Six (6) genes were fitted in the prognostic model based on the minimum cross-validation error in the cross-validation method. (Figs. 2A and B).

Then, all CC patients were assigned a risk score based on the following formula: risk score = (0.705 × expression of ENO3) + (0.528 × expression of ELOVL3) + (-0.416 × expression of ACOT11) + (0.410 × expression of ALAD) + (-0.264 × expression of ELOVL6) + (0.720 × expression of ACADL). We divided all CC patients into the high- and low-risk groups according to the median risk score. Principal component analysis (PCA) analysis showed that the genes involved in model construction could distinguish between patients at high and low risk, and the results were significantly better than those of fatty acid metabolism-related genes (Fig. 2C and D).

Validation of the fatty acid metabolism-related prognostic model in CC

To further validate the prognostic model, we evaluated the difference in survival between the high and low-risk groups. K-M survival analysis showed that OS in the high-risk group was significantly worse than that in the low-risk group (P < 0.001), and PFS was also worse than that in the low-risk group (P < 0.001) (Figs. 3A and B). Meanwhile, the AUC of 1-year, 2-year, and 3-year was 0.673, 0.727, and 0.746, respectively (Fig. 3C). The prognostic model AUC was significantly higher than clinical characteristics, including age, gender, and stage (Fig. 3D).

Then we performed the univariate and multivariate Cox regression analyses, we found that age (P = 0.008), stage (P < 0.001), T status (P < 0.001), N status (P < 0.001), M status (P < 0.001) and risk score (P < 0.001) were related to overall survival of CC in the univariate Cox regression analysis (Fig. 3E). While the multivariate Cox regression analysis showed that the prognostic model was the independent prognostic indicator (Fig. 3F).

GSEA Functional enrichment analysis

GEVA was used to verify further the richness and significant differences between low-risk and high-risk groups to explore differences in biological pathways between high and low-risk groups. Through KEGG database analysis, we found that it significantly enriched in many anabolic pathways and some important tumor-related pathways, such as glycosaminoglycan biosynthesis, glycan biosynthesis, fatty acid metabolism, notch signaling pathway, and P53 signaling pathway (Fig. 4A). The GO database, results showed that GSEA was significantly enriched in some metabolic pathways, including fatty acid metabolism, acrosomal metabolism, GTP metabolism, and so on (Fig. 4B).

Drug sensitivity analysis between the subgroups

Based on Cancer genomics of drug sensitivity in cancer (GDSC), the pRRophetic algorithm was used to predict drug sensitivity in high- and low-risk groups based on transcriptome data. The results found that the IC50 of (5Z)-7-Oxozeaenol, AG-014699, Axitinib, CEP-701, Cisplatin, Elesclomol, HG-5-113-01, HG-6-64-1, OSI-027, OSI-930, PF-4708671, PHA-665752, PI-103, WZ3105 were lower in the high-risk group, and the IC50 of JW-7-52-1, LY317615 in the high-risk group were higher (Fig. 5), which is helpful to explore chemotherapy drugs suitable for high- and low-risk group patients.

Immune cell infiltration and immune checkpoint

To further explore the correlation between risk score and immune status, we quantified the ssGSEA enrichment score of different immune cell subpopulations, related functions, or pathways. The results showed that macrophages, NK cells, T helper cells, T helper type 2 (Th2) cells, and T regulatory cells (Tregs) significantly differed in the high- and low-risk groups. These findings suggest that the high-risk group’s immunological function is more active (Fig. 6A). The expression of different immune checkpoints was subsequently examined in the high and low-risk groups, and the results showed that LAG3, PDCD1, and CTLA4 were increased in the high-risk group, while CD274 was no difference between the two groups (Fig. 6C). Finally, we assessed the efficacy of immunotherapy and found that the high-risk group had a higher TIDE score, a greater potential for immune escape, and a worse response to immunotherapy (Fig. 6B).

Validation of the expression levels of 6 fatty acid metabolism-related genes in CC and normal colon tissues

We obtained protein expression levels of genes related to fatty acid metabolism from immunohistochemical staining in the Human Protein Atlas (HPA) database, and the results showed that the expression level of ACOT11, ALAD, ELOVL6, and ENO3 in tumor tissues was higher than that in normal tissues, whereas the expression level of ACADL in normal tissues was higher than tumor tissues. ELOVL3 showed no significant difference between CC and normal colon tissues (Fig. 7). The protein expression of these prognostic genes in clinical tissue samples was consistent with the RNA sequencing data analyzed by the TCGA database (Fig. 8).

Nomogram system for CC patients

In order to get a comprehensive prediction model, we integrated these available factors in the nomogram system: age, gender, risk score, T status, N status, M status, and AJCC stage. The 3- and 5-years survival probability of CC was estimated. As shown in Fig. 9A, each factor had a weighted point, and the aggregate points indicated the predicted survival rates. For instance, 90 years old means 100 points, female means 60 points, high-risk means 62 points, T3 means 63 points, N2 means 81 points, M0 means 58 points, and stage 3 means 66 points, leading to a total point of 490 whose 1-year survival rate is 69.3%, the 3-year survival rate is 39.5%, the 5-year survival rate is 25.3%. Patients with higher points indicated a worse prognosis. Calibration plots of 1-, 3-, and 5-year survival rates in the nomogram model showed that the prediction of overall survival by the nomogram model was very close to the actual survival rate (Fig. 9B). We performed the ROC curve to evaluate the nomogram prediction system's predictive accuracy. The AUCs for 1 year, 3 years, and 5 years were 0.783, 0.799 and 0.792, indicating that the nomogram model was quite accurate in the prognosis prediction of CC patients (Fig. 9C). Finally, DCA was used to analyze the clinical validity of nomograms, which showed that nomograms were superior to other clinical indicators in the range of 0–45% threshold (Fig. 9D).

A common feature of tumor cells is the energy required to maintain cell proliferation through metabolic reprogramming[18]. Lipids not only constitute the basic structure of biological membranes but also function as signaling molecules and energy sources. Fatty acids are an important component of lipids, and studies have shown that tumor cells can support their rapid proliferation, migration, invasion, and metastasis through increased fatty acid uptake[19, 20]. CC is one of the malignant tumors with high morbidity and mortality. Exploring the role of different fatty acid metabolism genes in CC plays an important role in improving the therapeutic efficacy of CC and specifying individualized treatment.

In this study, we summarized fatty acid metabolism genes through three gene sets. Univariate and multivariate Cox proportional risk regression analyses were performed to screen out six fatty acid metabolism genes (ENO3、ELOVL3、ACOT11、ALAD、ELOVL6、ACADL) closely related to prognosis. The key genes related to fatty acid metabolism were further screened by LASSO regression and cross-validation, and the prognostic model was constructed. These six fatty acid metabolism genes were abnormally expressed in multiple tumor tissues and were closely related to tumor progression. However, the role of these genes in the prognosis of CC remains to be studied. ENO3 is an enzyme that catalyzes the formation of phosphoenolpyruvate from 2-phosphoglycerate. ENO3 can promote the proliferation and migration of colorectal cancer by enhancing cell glycolysis[21]. ELOVL3 and ELOVL6 are rate-limiting enzymes that catalyze the synthesis of very long-chain fatty acids. Dysfunctional ELOVL3 has been reported to be associated with various, including cancers. In prostate cancer, transcription enhancement of ELOVL3 is closely related to the invasion and migration of prostate cancer[22]. ELOVL6 promotes cell proliferation and Akt activation, enhancing carcinogenic activity in hepatocellular carcinoma (HCC), and is associated with poor prognosis in HCC patients[23]. ACOT11 can encode enzymatic hydrolysis of fatty acid acyl CoA esters into free fatty acids and CoA. The high expression of ACOT11 in lung cancer patients is positively correlated with poor prognosis. ACOT11 affects tumor invasion and migration and promotes cell apoptosis and cell cycle arrest through various signaling pathways[24]. Overexpression of ALAD in breast cancer patients can inhibit epithelial-mesenchymal transition phenotype, and inhibit breast cancer proliferation through TGFβ -mediated signaling pathway[25]. ACADL, as a key enzyme in the first step of mitochondrial fatty acid β oxidation, is down-regulated in liver cancer, and has a tumor-suppressor effect through Hippo/YAP signaling pathway[26]. ACADL can inhibit the expression of MMP14 through the STAT3 signaling pathway, thus inhibiting the metastasis of HCC[27]. However, the relevant role of this gene in fatty acid oxidation in CC is unclear. In this study, ACADL expression was down-regulated and correlated with the prognosis of CC patients. It was incorporated into the construction of the prognostic model for HCC patients, further demonstrating the important role of this gene in the development and progression of colon cancer.

The prognostic model was constructed based on the six genes mentioned above, and the patients were divided into two groups of high and low risk according to the median risk score. The combined PCA, ROC, and risk curve analysis suggested that the prognostic model had a good predictive value. Subsequently, risk score and clinical indicators were integrated (age, stage, T status, N status, M status), and the results suggested that risk score was an independent prognostic factor, which further confirmed that the model could predict the prognosis of CC patients.

Next, we further analyzed the function of immune cells in the high and low-risk populations, and the results showed that the immune cells in the high-risk group were more activated, and the related functions of immune cells were more active. Specifically, the expression level of macrophages and T helper cells was higher, and the expression level of Tregs was lower in the high-risk population. This may be related to the overactivation of the immune system in patients with early colon cancer. Previous research has shown that the increase of M2 polarization in macrophages can promote the growth of colon cancer[28–30]. Salman found that in the CC microenvironment, the infiltration of highly immunosuppressed Treg cells was significantly increased, and these increased Tregs may hinder the response of CRC patients to immune checkpoints blockade. However, the effects of different immune checkpoints inhibitory mechanisms on Treg level or activity need to be further studied[31]. Thus, targeting these immune cells offers the possibility of immunotherapy for colon cancer.

PD-1 (PDCD-1), PD-L1 (CD274), LAG3, and CTLA4 are important immune checkpoints. Abnormal activation of immune checkpoints on tumor cells contributes to the immune escape of tumor cells. Immune checkpoint inhibitors have been applied increasingly applied in various malignant tumors, proving their therapeutic potential. In melanoma patients[32], ipilimumab (CTLA4 inhibitor) and nivolumab (PD-1 inhibitor) have a significant survival benefit, and nivolumab plus ipilimumab has a more durable survival benefit. Our results showed that the expression levels of PD-1, LAG3, and CTLA4 were higher in high-risk patients, suggesting that targeted immune checkpoints such as PD-1, LAG3, and CTLA4 may improve the efficacy of immunotherapy in CC patients. Finally, TIDE scores of the high and low-risk groups were calculated respectively[33], and the results showed that the probability of immune escape was higher in the high-risk group, and the therapeutic effect of immune checkpoint blockade was worse.

Finally, we incorporated the prognostic score into the prediction model, and a nomogram was constructed. The Nomogram prediction model's accuracy was evaluated using calibration plots, ROC curve, and DCA. DCA is a new method to evaluate clinical predictive models[34]. The above three methods demonstrate the practicality of the prognostic model, which can help us timely identify patients with poor prognoses and specify individualized treatment plans to further improve the survival rate of CC patients.

In summary, we constructed prognostic scores based on six fatty acid metabolism-related genes, and a nomogram was constructed by incorporating prognostic scores into the prognostic model. In addition, we further explored differences in immune cell infiltration, immune checkpoint activation, and immunotherapy effects among patients at high and low risk. Our results suggest that fatty acid metabolism-related genes can be used as a new therapeutic target for CC and further improve the survival rate of CC patients through individualized therapy.

Ethics approval and consent to participate

Not applicable

Consent for publication

The author confirms that its publication has been approved by all co-authors.

Availability of data and materials

All data and materials can be obtained from https://portal.gdc.cancer.gov/.

Competing interests

The authors declare that they have no conflict of interest.

Funding

This manuscript is supported by the Scientific and Technological Research Foundation of Shaanxi Province, Key Research and Development Project, General project (JR, 2023-YBSF-666); supported by the Shaanxi Provincial Key Research and Development Plan Project (Key support projects Category A) (JR, 2021A011); supported by the Basic and Clinical Integration Innovation Project of Xi’an Jiao tong University (JR, YXJLRH2022006, YG, YXJLRH 2022004)

Authors' contributions

Juan Ren conceived and supervised the study; Xuan Wang, Min Chen, Shuheng Bai, Rong Li, Fengyuan Hu, Xiangxiang Zhang, yiping yang, Wenyang Li analyzed data; Ning Lan and Xuan Wang wrote the manuscript; Fang Wu, Wanyi Liu and Xuan Wang made manuscript revisions. All authors have read and approved the final version of this submission

Acknowledgements

Thank you for the help provided by the Translational Medicine Center of the First Affiliated Hospital of Xi'an Jiaotong University.

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No competing interests reported.

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Development of a fatty acid metabolism-related genes predictive model in colon cancer patients

Status:

Version 1

Abstract

Purpose

Method

Result

Conclusion

Figures

Introduction

Methods

Data Processing

Obtain fatty acid metabolism-related genes

Fatty acid metabolism-related prognostic model in CC patients

Drug sensitivity analysis

ssGSEA and TIDE

Establishment of nomogram system

Statistical analysis

Results

Characteristics of CC patients from the TCGA database

Identification of fatty acid metabolism-related genes in patients with CC

A prognostic model established with fatty acid metabolism-related genes in CC

Validation of the fatty acid metabolism-related prognostic model in CC

GSEA Functional enrichment analysis

Drug sensitivity analysis between the subgroups

Immune cell infiltration and immune checkpoint

Nomogram system for CC patients

Discussion

Declarations

References

Additional Declarations

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